As the population increases and various kinds of devices increase, noise pollution sources are increasing as well. People needs a map about noise which is capable to show current noise everywhere and serving as guidance for his/her traps and other daily schedules, just like a real-time road condition map. Meanwhile, the improvement of hardware storage capability and computational capability, the development of cloud technology and big data technology, and the development of various new data mining machine learning and even deep mining technology provides a basis for implementation of drawing a location-related noise map.
However, a noise map drawing method in the prior art can't distinguish indoor environmental noise from outdoor environmental noise, and goes against the improvement of drawing accuracy of the noise map. This is because the noise map drawing method in the prior art can't distinguish the indoor environment noise from the outdoor environment noise, it can only draw mixed environmental noise and generate the noise map merely instead. However, noise is different from information such as weather conditions, road conditions, and so on, noise is very sensitive to the position thereof; it is possible that a strength of the same noise may vary obviously at two places being separated by a wall, and the strength of the noise may change suddenly due to various reasons as well. Therefore, for drawing a location-related noise map, it needs to distinguish the indoor environmental noise from the outdoor environmental noise.